Results 51 to 60 of about 822,037 (305)

What to Do When Medical Evidence Can Only Be Generated Through Routine Data: An Example From Pediatric Oncology

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Children undergoing allogeneic stem cell transplantation often receive off‐label rituximab treatment for Epstein–Barr virus reactivation, using adult dosing without pediatric evidence. This project aims to develop a clinical decision support tool (CDSS) that provides evidence‐based dosing scenarios by analyzing real‐world patient data.
Birgit Burkhardt   +2 more
wiley   +1 more source

The MedSupport Multilevel Intervention to Enhance Support for Pediatric Medication Adherence: Development and Feasibility Testing

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction We developed MedSupport, a multilevel medication adherence intervention designed to address root barriers to medication adherence. This study sought to explore the feasibility and acceptability of the MedSupport intervention strategies to support a future full‐scale randomized controlled trial.
Elizabeth G. Bouchard   +8 more
wiley   +1 more source

Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

open access: yes, 2019
Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in \emph{zero-shot image retrieval and clustering}(ZSRC) where a good embedding is requested such that the unseen classes can be distinguished
Chen, Binghui, Deng, Weihong
core   +1 more source

Bounded-Distortion Metric Learning [PDF]

open access: yes, 2015
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of freedom to fit training data, it is prone to overfitting and numerical ...
Jia, Jiaya   +4 more
core  

Metric Learning Using Iwasawa Decomposition [PDF]

open access: yes2007 IEEE 11th International Conference on Computer Vision, 2007
Finding a good metric over the input space plays a fundamental role in machine learning. Most existing techniques use the Mahalanobis metric without incorporating the geometry of positive matrices and experience difficulties in the optimization procedure.
Bing, Jian, Baba C, Vemuri
openaire   +2 more sources

Prolonged Corrected QT Interval as an Early Electrocardiographic Marker of Cyclophosphamide‐Induced Cardiotoxicity in Pediatric Hematology and Oncology Patients

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Cyclophosphamide (CY) is associated with potentially fatal cardiotoxicity, yet no electrocardiographic indices have been established for early detection of CY‐induced cardiomyopathy. This study aimed to determine whether corrected QT interval (QTc) prolongation can predict early onset of CY‐related cardiac dysfunction in pediatric ...
Junpei Kawamura   +5 more
wiley   +1 more source

Kernelised reference‐wise metric learning

open access: yesElectronics Letters, 2017
Unlike the doublet or triplet constraints, a novel kernelised reference‐wise metric learning is proposed by constructing reference‐wise constraints, which contain similarity information of each sample to all reference samples.
Meng Wu, Kai Luo, Daijin Li, Jun Zhou
doaj   +1 more source

Parametric Local Metric Learning for Nearest Neighbor Classification [PDF]

open access: yes, 2012
We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics.
Kalousis, Alexandros   +2 more
core  

Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization

open access: yes, 2018
We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning. Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to ...
Banerjee, Biplab   +2 more
core   +1 more source

Similarity Metric Learning [PDF]

open access: yes, 2012
Similarity metric learning models the general semantic similarities and distances between objects and classes of objects (e.g. persons) in order to recognise them. Different strategies and models based on Deep Learning exist and generally consist in learning a non-linear projection into a lower dimensional vector space where the semantic similarity ...
Duffner, Stefan   +3 more
openaire   +2 more sources

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